Emergent service provisioning and demand estimation through self-organizing agent communities

ABSTRACT

A major challenge within open markets is the ability to satisfy service demand with an adequate supply of service providers, especially when such demand may be volatile due to changing requirements, or fluctuations in the availability of services. Ideally, this supply and demand should be balanced; however, when consumer demand changes over time, and providers independently choose which services they provide, a coordination problem known as 'herding' can arise bringing instability to the market. This behavior can emerge when consumers share similar preferences for the same providers, and thus compete for the same resources. Likewise, providers which share estimates of fluctuating demand may respond in unison, withdrawing some services to introduce others, and thus oscillate the available supply around some ideal equilibrium. One approach to avoid this unstable behavior is to limit the flow of information between agents, such that they possess an incomplete and subjective view of the local service availability. We propose a model of an adaptive service-offering mechanism, in which providers adapt their choice of services offered to consumers, based on perceived demand. By varying the volume of information shared by agents, we demonstrate that a co-adaptive equilibrium can be achieved, thus avoiding the herding problem. As the knowledge that agents possess is limited, they self-organise into community structures that support locally shared information. We demonstrate that such a model is capable of reducing instability in service demand and thus increase utility (based on successful service provision) by up to 59%, when compared to the use of globally available information.

REFERENCES

• 1: W. B. Arthur. Inductive reasoning and bounded rationality. American Economic Review, 84:406--411, 1994.
• 2: S. Brueckner and H. V. D. Parunak. Self-organizing MANET management. In G. D. Marzo, A. Karageorgos, O. F. Rana, and F. Zambonelli, editors, Engineering Self-Organising Systems, pages 1--16. Springer, 2003.
• 3: Sven A. Brueckner , H. Van Dyke Parunak, Information-driven phase changes in multi-agent coordination, Proceedings of the second international joint conference on Autonomous agents and multiagent systems, July 14-18, 2003, Melbourne, Australia [doi>10.1145/860575.860740]
• 4: Edmund H. Durfee, Scaling Up Agent Coordination Strategies, Computer, v.34 n.7, p.39-46, July 2001 [doi>10.1109/2.933502]
• 5: S. Guerin and D. Kunkle. Emergence of constraint in self-organizing systems. Journal of Nonlinear Dynamics, Psychology, and Life Sciences, 8, 2004.
• 6: T. Hogg and B. A. Huberman. Controlling chaos in distributed systems. IEEE Transactions on Systems, Man and Cybernetics, 21:1325--1332, 1991.
• 7: N. H. Packard. Adaptation toward the edge of chaos. In A. M. J. A. Kelso and M. Shlesinger, editors, Dynamic patterns in complex systems, pages 293--301. World Scientific, 1988.
• 8: H. V. D. Parunak and S. A. Brueckner. Engineering swarming systems. In F. Bergenti, M.-P. Gleizes, and F. Zambonelli, editors, Methodologies and Software Engineering for Agent Systems, pages 341--376. Kluwer, 2004.
• 9: D. Pynadath and M. Tambe. The communicative multiagent team decision problem: analyzing teamwork theories and models. Journal of Artificial Intelligence Research, 16:389--423, 2002.
• 10: S. Sen, S. Roychowdhury, and N. Arora. Effects of local information on group behavior. In Proceedings of the Second International Conference on Multi-Agent Systems, pages 315--321. AAAI Press, Menlo Park, CA, 1996.
• 11: Onn Shehory , Sarit Kraus, Methods for task allocation via agent coalition formation, Artificial Intelligence, v.101 n.1-2, p.165-200, May 1998 [doi>10.1016/S0004-3702(98)00045-9]
• 12: Sebastian Stein , Nicholas R. Jennings , Terry R. Payne, Flexible service provisioning with advance agreements, Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, May 12-16, 2008, Estoril, Portugal
• 13: S. Stepney. Critical critical systems. In S. S. Ali E. Abdallah, Peter Ryan, editor, Formal Aspects of Security: FASec, volume 2629 of LNCS. Springer, 2003.
• 14: Peter Stone , Manuela M. Veloso, Layered Learning and Flexible Teamwork in RoboCup Simulation Agents, RoboCup-99: Robot Soccer World Cup III, p.495-508, January 2000
• 15: K. Sycara, K. Decker, and M. Williamson. Middle-agents for the internet. In Proceedings of IJCAI-97, January 1997.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Design, Experimentation

Keywords:
adaptive service markets, self-organising, service provisioning